g without the accepted clinical criteria for massive pulmonary embolism, saddle pulmonary embolism has a very high inhospital mortality. Ventilation/perfusion scan is unable to diagnose saddle pulmonary embolism. Visualized right heart thrombi portend an even higher inhospital mortality.To describe the epidemiology of superinfections (occurring &gt; 48?hr after hospital admission) and their impact on the ICU and 28-day mortality in patients with coronavirus disease 2019 with acute respiratory distress syndrome, requiring mechanical ventilation.Retrospective analysis of prospectively collected observational data.
University-affiliated adult ICU.
Ninety-two coronavirus disease 2019 patients admitted to the ICU from February 21, 2020, to May 6, 2020.
None.
The prevalence of superinfection at ICU admission was 21.7%, and 53 patients (57.6%) had at least one superinfection during ICU stay, with a total of 75 (82%) ventilator-associated pneumonia and 57 (62%) systemic infections. The most common pathogens responsible for ventilator-associated pneumonia were (= 26, 34.7%) and (= 14, 18.7%). Bloodstream infection occurred in 16 cases, including methicillin-resistant (= 8, 14.0%), species (= 6, 10.5%), and species (= 2, 3.5%). Fungal infections occurred in 41 cases, including 36 probable (30 by , six by ) and five proven invasive candidiasis (three , two ). Presence of bacterial infections (odds ratio, 10.53; 95% CI, 2.31-63.42; = 0.005), age (odds ratio, 1.17; 95% CI, 1.07-1.31; = 0.001), and the highest Sequential Organ Failure Assessment score (odds ratio, 1.27; 95% CI, 1.06-1.63; = 0.032) were independently associated with ICU or 28-day mortality.
Prevalence of superinfections in coronavirus disease 2019 patients requiring mechanical ventilation was high in this series, and bacterial superinfections were independently associated with ICU or 28-day mortality (whichever comes first).
Prevalence of superinfections in coronavirus disease 2019 patients requiring mechanical ventilation was high in this series, and bacterial superinfections were independently associated with ICU or 28-day mortality (whichever comes first).To develop and evaluate fully automatic scan range delimitation for chest CT by using deep learning.
For this retrospective study, scan ranges were annotated by two expert radiologists in consensus in 1149 (mean age, 65 years ± 16 [standard deviation]; 595 male patients) chest CT topograms acquired between March 2002 and February 2019 (350 with pleural effusion, 376 with atelectasis, 409 with neither, 14 with both). A conditional generative adversarial neural network was trained on 1000 randomly selected topograms to generate virtual scan range delimitations. https://www.selleckchem.com/products/iacs-010759-iacs-10759.html On the remaining 149 topograms the software-based scan delimitations, scan lengths, and estimated radiation exposure were compared with those from clinical routine. For statistical analysis an equivalence test (two one-sided tests) was used, with equivalence limits of 10 mm.
The software-based scan ranges were similar to the radiologists' annotations, with a mean Dice score coefficient of 0.99 ± 0.01 and an absolute difference of 1.8 mm ± 1.9 anediatrics, CT, Computer Applications-Detection/Diagnosis, Convolutional Neural Network (CNN), Lung, Radiation Safety, Segmentation, Supervised learning, Thorax ©?RSNA, 2021Supplemental material is available for this article.To develop and validate a neural network for automated detection and segmentation of intracranial metastases on brain MRI studies obtained for stereotactic radiosurgery treatment planning.
In this retrospective study, 413 patients (average age, 61 years ± 12 [standard deviation]; 238 women) with a total of 5202 intracranial metastases (median volume, 0.05 cm; interquartile range, 0.02-0.18 cm) undergoing stereotactic radiosurgery at one institution were included (January 2017 to February 2020). A total of 563 MRI examinations were performed among the patients, and studies were split into training (= 413), validation (= 50), and test (= 100) datasets. A three-dimensional (3D) U-Net convolutional network was trained and validated on 413 T1 postcontrast or subtraction scans, and several loss functions were evaluated. After model validation, 100 discrete test patients, who underwent imaging after the training and validation patients, were used for final model evaluation. Performance for detection overall and 98.4% for metastases larger than 6 mm.
A 3D U-Net-based convolutional neural network was able to segment brain metastases with high accuracy and perform detection at the level of human interrater reliability for metastases larger than 6 mm.Adults, Brain/Brain Stem, CNS, Feature detection, MR-Imaging, Neural Networks, Neuro-Oncology, Quantification, Segmentation©?RSNA, 2021.
A 3D U-Net-based convolutional neural network was able to segment brain metastases with high accuracy and perform detection at the level of human interrater reliability for metastases larger than 6 mm.Keywords Adults, Brain/Brain Stem, CNS, Feature detection, MR-Imaging, Neural Networks, Neuro-Oncology, Quantification, Segmentation©?RSNA, 2021.In recent years, deep learning techniques have been applied in musculoskeletal radiology to increase the diagnostic potential of acquired images. Generative adversarial networks (GANs), which are deep neural networks that can generate or transform images, have the potential to aid in faster imaging by generating images with a high level of realism across multiple contrast and modalities from existing imaging protocols. This review introduces the key architectures of GANs as well as their technical background and challenges. Key research trends are highlighted, including (a) reconstruction of high-resolution MRI; (b) image synthesis with different modalities and contrasts; (c) image enhancement that efficiently preserves high-frequency information suitable for human interpretation; (d) pixel-level segmentation with annotation sharing between domains; and (e) applications to different musculoskeletal anatomies. In addition, an overview is provided of the key issues wherein clinical applicability is challenging to capture with conventional performance metrics and expert evaluation.